Inspiration

Traditionally, ecology and biology has been largely split into the purely empirical (generating or analysing data with only informal use of models) and the purely theoretical (analysing models that have been, at best, only informally constrained with data). However, to create a precise, predictive, understanding of ecological and biological systems it is necessary to bridge this gap, using data to formally parameterize, and select between, competing models.

Features

• Specify parameters, define the likelihood, and Filzbach does the rest
• The automatic adaptive MCMC sampling algorithm copes with a wide range of different problems with no need for any manual tuning
• Automatic handling of multiple chains, testing for convergence, calculation of MLEs, posterior means and credible intervals on all parameters; and AIC, BIC, DIC
• Easy error propagation of parameter uncertainty through any model
• Fast and robust compared to commonly used alternatives
• Comes with a library of easy to use parameter distributions -- but can be extended include any others, so long as they are written in C

To try online, download, and learn more, see the Filzbach section of our new tool site.

Filzbach was developed by Drew Purves and Vassily Lyutsarev, within the Computational Science Lab at Microsoft Research, Cambridge.